JOIN · ASPICON Newsarchiv https://www.aspicon.de/news/tag/join/ Database Technology and Service Mon, 07 Oct 2024 06:54:50 +0000 de hourly 1 https://wordpress.org/?v=7.0 https://www.aspicon.de/wp-content/uploads/2023/12/favicon_white_verlauf_2-50x50.png JOIN · ASPICON Newsarchiv https://www.aspicon.de/news/tag/join/ 32 32 SQL Tuning Tipp: Analytische Funktionen https://www.aspicon.de/news/sql-tuning-tipp-analytische-funktionen/ Thu, 31 Oct 2019 07:55:00 +0000 https://www.aspicon.de/?p=7362 Analytische Funktionen sind ein mächtiges Werkzeug zur Formulierung von Abfragen, die nicht oder nur aufwändig mit herkömmlichen Aggregatfunktionen zu formulieren wären.

Der Beitrag SQL Tuning Tipp: Analytische Funktionen erschien zuerst auf ASPICON.

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SQL Tuning Tipp: Analytische Funktionen

Hinsichtlich Performance liegt die Stärke analytischer Funktionen darin, dass die analytischen Aggregate gleich parallel zur eigentlichen Abfrage berechnet werden – statt separater Berechnung in Subqueries verbunden mit JOIN-Kosten. Ich habe hier einige Beispiele zusammengestellt, welche das verdeutlichen sollen:

Beispiel 1: Ermittle die Differenz der Gehälter zum Abteilungsdurchschnitt

Mit herkömmlichen Aggregatfunktionen:

SELECT first_name, last_name, salary emp_salary, avg_salary dept_avg, salary - avg_salary salary_diff
FROM employees e JOIN (
SELECT department_id, round(AVG(salary)) avg_salary
FROM employees
GROUP BY department_id) USING ( department_id );

----------------------------------------------------------------------------------
| Id  | Operation            | Name      | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT     |           |   339 | 19323 |     7  (15)| 00:00:01 |
|*  1 |  HASH JOIN           |           |   339 | 19323 |     7  (15)| 00:00:01 |
|   2 |   VIEW               |           |    11 |   286 |     4  (25)| 00:00:01 |
|   3 |    HASH GROUP BY     |           |    11 |   176 |     4  (25)| 00:00:01 |
|   4 |     TABLE ACCESS FULL| EMPLOYEES |   107 |  1712 |     3   (0)| 00:00:01 |
|   5 |   TABLE ACCESS FULL  | EMPLOYEES |   107 |  3317 |     3   (0)| 00:00:01 |
----------------------------------------------------------------------------------

Mit analytischer Funktion:

SELECT first_name, last_name, salary emp_salary,
round(avg(salary) over (partition by department_id)) dept_avg,
salary - round(avg(salary) over (partition by department_id)) salary_diff
FROM employees;

--------------------------------------------------------------------------------
| Id  | Operation          | Name      | Rows  | Bytes | Cost (%CPU)| Time     |
--------------------------------------------------------------------------------
|   0 | SELECT STATEMENT   |           |   107 |  3317 |     4  (25)| 00:00:01 |
|   1 |  WINDOW SORT       |           |   107 |  3317 |     4  (25)| 00:00:01 |
|   2 |   TABLE ACCESS FULL| EMPLOYEES |   107 |  3317 |     3   (0)| 00:00:01 |
--------------------------------------------------------------------------------

Beispiel 2: Bestimme das Ranking der Mitarbeiter innerhalb ihrer Abteilung nach Gehalt

Mit herkömmlichen Aggregatfunktionen:

nicht abbildbar.

Mit analytischer Funktion:

SELECT first_name, last_name, department_name, salary,
RANK() OVER (PARTITION BY department_id ORDER BY salary DESC) rang
FROM employees JOIN departments using (department_id);

---------------------------------------------------------------------------------------------
| Id  | Operation                     | Name        | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT              |             |   106 |  4982 |     7  (29)| 00:00:01 |
|   1 |  WINDOW SORT                  |             |   106 |  4982 |     7  (29)| 00:00:01 |
|   2 |   MERGE JOIN                  |             |   106 |  4982 |     6  (17)| 00:00:01 |
|   3 |    TABLE ACCESS BY INDEX ROWID| DEPARTMENTS |    27 |   432 |     2   (0)| 00:00:01 |
|   4 |     INDEX FULL SCAN           | DEPT_ID_PK  |    27 |       |     1   (0)| 00:00:01 |
|*  5 |    SORT JOIN                  |             |   107 |  3317 |     4  (25)| 00:00:01 |
|   6 |     TABLE ACCESS FULL         | EMPLOYEES   |   107 |  3317 |     3   (0)| 00:00:01 |
---------------------------------------------------------------------------------------------

FIRST_NAME           LAST_NAME            DEPARTMENT_NAME          SALARY       RANG
-------------------- -------------------- -------------------- ---------- ----------
Jennifer             Whalen               Administration             4400          1
Michael              Hartstein            Marketing                 13000          1
Pat                  Fay                  Marketing                  6000          2
Den                  Raphaely             Purchasing                11000          1
Alexander            Khoo                 Purchasing                 3100          2
Shelli               Baida                Purchasing                 2900          3
Sigal                Tobias               Purchasing                 2800          4

Beispiel 3: Ermittle das zweithöchste Gehalt je Abteilung

Mit herkömmlichen Aggregatfunktionen:

SELECT department_name, max(salary) salary – von diesen Gehältern das größte
FROM employees e JOIN departments d on (e.department_id=d.department_id)
-- alle Gehälter, die kleiner als das größte sind
WHERE salary < ANY (
SELECT salary
FROM employees
WHERE department_id=e.department_id)
GROUP BY department_name;

----------------------------------------------------------------------------------------------
| Id  | Operation                      | Name        | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT               |             |     5 |   240 |    10  (20)| 00:00:01 |
|   1 |  HASH GROUP BY                 |             |     5 |   240 |    10  (20)| 00:00:01 |
|*  2 |   HASH JOIN SEMI               |             |     5 |   240 |     9  (12)| 00:00:01 |
|   3 |    MERGE JOIN                  |             |   106 |  3392 |     6  (17)| 00:00:01 |
|   4 |     TABLE ACCESS BY INDEX ROWID| DEPARTMENTS |    27 |   432 |     2   (0)| 00:00:01 |
|   5 |      INDEX FULL SCAN           | DEPT_ID_PK  |    27 |       |     1   (0)| 00:00:01 |
|*  6 |     SORT JOIN                  |             |   107 |  1712 |     4  (25)| 00:00:01 |
|   7 |      TABLE ACCESS FULL         | EMPLOYEES   |   107 |  1712 |     3   (0)| 00:00:01 |
|   8 |    TABLE ACCESS FULL           | EMPLOYEES   |   107 |  1712 |     3   (0)| 00:00:01 |
----------------------------------------------------------------------------------------------

Mit analytischer Funktion:

SELECT distinct department_name, salary
FROM (
SELECT department_name, salary, DENSE_RANK() OVER (PARTITION BY department_id ORDER BY salary DESC) rang
FROM employees JOIN departments USING (department_id))
WHERE rang=2;

-----------------------------------------------------------------------------------------------
| Id  | Operation                       | Name        | Rows  | Bytes | Cost (%CPU)| Time     |
-----------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                |             |    27 |  1026 |     8  (38)| 00:00:01 |
|   1 |  HASH UNIQUE                    |             |    27 |  1026 |     8  (38)| 00:00:01 |
|*  2 |   VIEW                          |             |   106 |  4028 |     7  (29)| 00:00:01 |
|*  3 |    WINDOW SORT PUSHED RANK      |             |   106 |  3392 |     7  (29)| 00:00:01 |
|   4 |     MERGE JOIN                  |             |   106 |  3392 |     6  (17)| 00:00:01 |
|   5 |      TABLE ACCESS BY INDEX ROWID| DEPARTMENTS |    27 |   432 |     2   (0)| 00:00:01 |
|   6 |       INDEX FULL SCAN           | DEPT_ID_PK  |    27 |       |     1   (0)| 00:00:01 |
|*  7 |      SORT JOIN                  |             |   107 |  1712 |     4  (25)| 00:00:01 |
|   8 |       TABLE ACCESS FULL         | EMPLOYEES   |   107 |  1712 |     3   (0)| 00:00:01 |
-----------------------------------------------------------------------------------------------
Hier findest du weitere Posts zu den Themen SQL Tuning bzw. Performance Tuning aus unserem News Bereich.
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Der Beitrag SQL Tuning Tipp: Analytische Funktionen erschien zuerst auf ASPICON.

]]>